Download DNA methylation: potential biomarker in Hepatocellular Carcinoma

Mah and Lee Biomarker Research 2014, 2:5
http://www.biomarkerres.org/content/2/1/5
REVIEW
Open Access
DNA methylation: potential biomarker in
Hepatocellular Carcinoma
Way-Champ Mah1,2,3 and Caroline GL Lee1,2,3,4*
Abstract
Hepatocellular Carcinoma (HCC) is one of the most common cancers in the world and it is often associated with
poor prognosis. Liver transplantation and resection are two currently available curative therapies. However, most
patients cannot be treated with such therapies due to late diagnosis. This underscores the urgent need to identify
potential markers that ensure early diagnosis of HCC. As more evidences are suggesting that epigenetic changes
contribute hepatocarcinogenesis, DNA methylation was poised as one promising biomarker. Indeed, genome wide
profiling reveals that aberrant methylation is frequent event in HCC. Many studies showed that differentially
methylated genes and CpG island methylator phenotype (CIMP) status in HCC were associated with clinicopathological
data. Some commonly studied hypermethylated genes include p16, SOCS1, GSTP1 and CDH1. In addition,
studies have also revealed that methylation markers could be detected in patient blood samples and associated
with poor prognosis of the disease. Undeniably, increasing number of methylation markers are being discovered
through high throughput genome wide data in recent years. Proper and systematic validation of these
candidate markers in prospective cohort is required so that their actual prognostication and surveillance value
could be accurately determined. It is hope that in near future, methylation marker could be translate into clinical
use, where patients at risk could be diagnosed early and that the progression of disease could be more correctly
assessed.
Keywords: Epigenetics, Methylation, Biomarker, CIMP, Hepatocellular carcinoma, Prognosis, Diagnosis
Introduction
Hepatocellular Carcinoma (HCC) is one of the most frequent cancers in the world and annually, about 600,000
patients died of liver cancer [1]. This disease is often associated with poor prognosis because patients are either
diagnosed at very late stage or experienced recurrence
after resection [2]. In fact, more than half of HCC patients died within 12 months post diagnosis, and less
than 6% of them have an average survival rate of 5 years
[3]. Liver transplantation and resection are the only two
curative therapies available; however, in order to qualify
for such therapies, patients need to be diagnosed early
with HCC [4]. Presently, serum alpha-fetoprotein (AFP)
concentration and hepatic ultrasonography are used in
* Correspondence: [email protected]
1
Department of Biochemistry, Yong Loo Lin School of Medicine, National
University of Singapore, Singapore 117597, Singapore
2
Division of Medical Sciences, Humphrey Oei Institute of Cancer Research,
National Cancer Centre Singapore, Level 6, Lab 5, 11 Hospital Drive,
Singapore 169610, Singapore
Full list of author information is available at the end of the article
HCC surveillance program, where high risk patients are
screened for HCC in every six months [5]. As for actual
diagnosis, invasive biopsy and expensive imaging tools
such as ultrasonography, spiral computed tomography
(CT) and magnetic resonance imaging (MRI) are used
[4]. AFP measurement is merely used as adjunct diagnostic tool because of its variability in specificity and
sensitivity [5,6]. Equally important to note is that apart
from AFP level and tumor staging classification such as
the Barcelona Clinic Liver Cancer (BCLC) staging system, there is no good prognostic marker that can classify
patients and predict survival outcome [7-9]. The large
number of HCC associated deaths clearly reflects the
shortcomings of current diagnostic and prognostic tools.
This underscores the importance of discovering novel
and effective biomarkers that can improve overall clinical management of HCC.
With the advance of genomic technologies, plethora of
molecular data is now available for translational research.
Gene expression signatures and microRNA profiles are
© 2014 Mah and Lee; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
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just some examples of molecular data that were actively
explored as potential biomarkers for HCC [10-15]. In this
review, we will focus specifically on DNA methylation,
another potential biomarker that was shown to be implicated in HCC.
Review
Aberrant DNA methylation in HCC
Studies have identified a few somatic mutations in HCC,
for instance, mutations in TP53 [16,17], CTNNB1 [18,19],
and AXIN1 [20,21]. However, frequencies of these mutations are inconsistent and rare, some occur only in certain
subtypes of tumor [22]. The lack of common genetic marker associated with HCC cases strongly suggests that epigenetic alterations such as aberrant DNA methylation
could be the alternative factor contributing towards liver
carcinogenesis. DNA methylation occurs when a methyl
group is attached to the 5th carbon of cytosine nucleotide
and this process is catalyzed by DNA methyltransferases
(DNMTs), in which S-adenosyl-methionine (SAM) acts as
a methyl donor (Figure 1) [23]. Deregulation of DNA
methylation was shown to be associated with many cancers, as was first proposed by Andrew Feinberg and Bert
Vogelstein in 1983 [24]. The two most common forms of
aberrant methylation are global hypomethylation and sitespecific hypermethylation. In HCC, such deregulations are
frequently observed as well. Global hypomethylation in
liver cancer affects the structural-nuclear function by promoting chromosomal and genomic instability, while regional hypermethylation is often associated with silencing
of tumor suppressor genes [25]. Studies have revealed that
etiological factors like Hepatitis virus infection may lead
to aberrant DNA methylation in cancerous tissues [26,27].
DNA methyltransferases such as DNMT1, DNMT3A and
DNMT3B were also shown to be up-regulated in liver
cancer [28,29]. Whether increased expression of DNMTs
associated with aberrant methylation of genes is still a
matter of controversy as the exact mechanism has yet to
be elucidated [29,30]. Subsequent sections will summarize
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respective studies on aberrant DNA methylation in hepatocarcinogenesis and the full list of studies can be found
in Additional file 1: Table S1.
Genome wide studies on methylation profile of HCC
Present technologies allow researchers to profile methylation in a genome wide manner. Two commonly used
methods in HCC methylation profiling include hybridization of bisulfite converted DNA on beadarray [31,32]
and enrichment of methylated DNA either by enzymatic
digestion [33,34] or antibody pull-down [35], followed by
promoter array profiling. Even though high throughput
sequencing is becoming more available, presently, no
study has yet to use this approach to map the methylome
of HCC. Table 1 briefly summarizes the strengths and
limitations of each profiling method. A few pivotal genome wide methylation studies using these approaches
are highlighted below.
In 2008, Gao et al. adopted methylated CpG island amplification microarray (MCAM) method to identify 719
genes that were differentially methylated between tumors
and adjacent non-tumors [36]. They used pyrosequencing
to validate their observations found by MCAM. Genes
such as RASSF1A, CDKN2A and CCNA1 were successfully validated to be highly methylated in cancer tissue
compared to adjacent non-tumor and normal liver tissues.
In subsequent year, Lu et al. used differential methylation
hybridization (DMH) method to locate 38 hypomethylated
and 27 hypermethylated regions. Using Methylation specific PCR (MSP) method, they validated the methylation
status of KLK10 and OXGR1 in tumors, and found that
hypermethylation of KLK10 was associated with Hepatitis
C virus (HCV) infection and cirrhosis [37]. Around the
same time, studies by Deng et al. and Stefanska et al. used
a slightly different method called methylated DNA immunoprecipitation microarray (MeDIP-chip) to locate aberrant methylation in HCC. Deng et al. used MassArray®
method to validate hypermethylation of DUSP4, NPR1
and CYP24A1 in HCC, and correlate methylation status
Figure 1 Structures of deoxycytidine and 5-methyl-deoxycytidine. DNA methylation occurs when a methyl group is attached to the 5th
carbon of cytosine, where DNMT serves as enzyme and SAM acts as the methyl group donor.
Microarray
based
Beadarray
based
Platform
Features
Number of
regions
analysed
per sample
Methylation
information
on site
specific CpG
loci
Methylation
information
on non-CpG
loci
Advantages
Disadvantages
Methylated CpG
Island Amplification
and Microarray
(MCAM-chip)
Enzyme-based techniques
that rely on restriction
enzymes (SmaI and XmaI)
followed by profiling on
promoter array
~25,000 human
promoters (depends on
array density)
No
No
Do not require bisulfite
conversion, good coverage on
region with low CpG density.
Differential Methylation
Hybridization and
Microarray (DMH-chip)
Enzyme-based techniques
that rely on restriction
enzymes (MseI and BstUI)
followed by profiling on
promoter array
Require substantial quantities of
input genomic DNA, low sample
throughput, do not report
methylation status at single
nucleotide level, bias may
occur due to genomic distribution
of CpG loci, limited to mostly
promoter regions.
Methylated DNA
Immunoprecipitation
and Microarray
(MeDIP-chip)
Immunoprecipitation of
methylated DNA with a
monoclonal antibody
followed by profiling on
promoter array
GoldenGate
Bisulfite convertion of DNA
followed by microbead
based microarray
~1,500 CpG sites
Yes
No
Require minimum input
genomic DNA, high
sample throughput,
provide methylation
status at CpG loci,
fairly accurate and
reproducible.
Bisulfite treatment may not be
complete, bisulfite treatment
caused DNA degradation, limited
to mostly promoter regions.
Yes
Yes
High resolution mapping of
methylation status at single
nucleotide level, no cross
hybridization bias.
Bisulfite treatment may not be
complete, bisulfite treatment
caused DNA degradation, low
sample throughput, expensive,
complex bioinformatic analysis.
Infinium 27K
Infinium 450K
High
throughput
sequencing
Bisulfite sequencing
~27,000 CpG sites
~450,000 CpG sites
Bisulphite conversion of
DNA followed by capture
and high throughput
sequencing
Whole genome
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Table 1 Different methods in genome-wide methylation profiling
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of these genes with recurrence free survival [38]. Stefanska
et al., on the other hand, delineated the profile of promoter hypomethylation in HCC and validated AKR1B10,
CENPH, MMP9, MMP12, PAGE4, S100A5, MMP2 and
NUPR1 to be hypomethylated in liver cancer using pyrosequencing [39]. Earlier genome wide studies utilised promoter microarray to map out differentially methylated
regions. As a result, such arrays could not provide information on site specific CpG dinucleotides that were
aberrantly methylated. Additional validation steps such as
pyrosequencing and MassArray® were required before one
could locate the exact deregulated CpG sites. Nonetheless,
as technology of methylation profiling matures over recent
years, many studies could now report genome wide methylation status of HCC at single-nucleotide resolution
(Table 2). These studies mainly used the beadarray technology developed by Illumina®. As shown in Table 2, the
most recent studies by Song et al., Zhang et al. and Shen
et al. reported the mapping of more than 485000 CpG
sites, the highest throughput so far, in HCC.
Genome wide methylation profiling provides wealth of
information for downstream analysis. Using these high
throughput data, researchers could efficiently separate tumors from adjacent non-tumors [39-47], cirrhotic liver
from HCC [48,49], and cluster the tumors according to
their risk factors such as viral infection [38,43,46] and alcohol consumption [42,43]. Novel tumor suppressor genes
that were silenced by methylation could also be uncovered
through genome wide studies, as shown by studies in
[37,50-53]. As reports on genome wide methylation profile
of HCC patients using serum DNA begin to emerge
[41,42], it is hoped that these high throughput data could
accelerate the process of biomarker discovery.
Methylation as prognostic marker
From 2003 to 2013, many studies have published on the
prognostic values of DNA methylation in HCC. These
studies are summarized in Additional file 1: Table S1. Due
to space constraints, within this review, only a few examples will be highlighted. One of the early studies was reported by Yang et al. Their group profiled methylation
status of 9 genes, namely GSTP1, SOCS1, CDH1, APC,
p15, p16, p14, p73 and RAR-β in 51 HCC samples using
methylation-specific polymerase chain reaction (MSP)
[54]. Among these genes, methylation of SOCS1, APC
and p15 were shown to be more frequently observed in
HCV-positive HCC patients compared to HBV/HCVnegative HCC. Another group from Korea, Lee et al.
examined the methylation level of CpG loci in 14 genes in
sixty HCC paired samples and found that methylation of
GSTP1 and CDH1 were associated with poorer overall
survival [55]. Similarly, Yu et al. used MSP to identify
methylation level of 24 genes in 28 HCC samples from
a Chinese population. They successfully showed that
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methylation of AR, DBCCR1, IRF7, OCT6, p73, and p16
were associated with late stage HCC [56]. These three
studies laid a strong basis for subsequent methylation analysis. Many studies have since then attempted to associate
clinical parameters with DNA methylation, particularly on
genes that were validated in these three studies. Subsequent paragraphs will outline 4 of these genes, namely,
p16, SOCS1, GSTP1 and CDH1 (Table 3).
Commonly studied methylation marker genes
p16 (CDKN2A) is one of the most reported genes that
was shown to be hypermethylated and associated with clinical parameters in HCC. It is a tumor suppressor gene that
plays a role in cell cycle regulation [57]. It was methylated
in many other cancers as well [58]. Beside earlier study by
Yu et al. [56], Shim et al. [59] and Su et al. [60] also reported that methylation level of p16 was associated with
advanced stage of HCC. They showed that methylation of
p16 gene increased from cirrhotic tissue to HCC. Studies
have also shown that hepatitis virus positive HCC samples
have higher p16 methylation compared to HCC with no
viral infection [61-64]. Zhu et al. even further showed that
HBx gene, a protein coded by HBV, was associated with
methylation of p16 in HBV positive HCC samples [65].
Clearly, environment factors such as viral infection could
possibly disturb the epigenetic profile of the liver and contribute towards carcinogenesis. In addition, vascular invasion [61] and tumor differentiation [66] were also shown
to be associated with p16 methylation. As vascular invasiveness and tumor differentiation were both strong predictors of survival in HCC [67-69], it is not surprising that
hypermethylation of p16 in HCC patients was shown to
have worse disease free survival as well [70].
Another frequently studied prognostic marker is SOCS1
methylation. SOCS1 gene was shown to be negative
regulator of JAK/STAT pathway and its suppression by
hypermethylation promotes cell growth [71]. SOCS1 methylation was correlated with progression of HCC [72],
age [73,74] and tumor size [73,75]. Moreover, as mentioned in earlier paragraph, SOCS1 methylation was
shown by Yang et al. to be associated with HCV infected
HCC [54]. Concurring their study, Nishida et al. [76] and
Ko et al. [73] also revealed that SOCS1 methylation was
more prevalent in HCV infected HCC compared to noninfected HCC. Interestingly, Chu et al. [75] and Okochi
et al. [77] did not find this association to be significant in
their studies; instead, they found liver cirrhosis in HCC to
be closely linked to hypermethylation of SOCS1. As HCV
infection may lead to liver cirrhosis [78], more studies are
required to ascertain this pathological link between HCV
infection, SOCS1 methylation and HCC progression.
GSTP1 belongs to Glutathione S-transferases family,
where it plays a role in protecting cells against damage induced by carcinogens, and modulating signal transduction
Promoter microarray
Discovery method
MCAM-chip
HCC patients (n)
Validation method
Validated genes
Publication
Year
10
Pyrosequencing
RASSF1A, p16, TBX4, MMP14, GNA14, SLC16A5, CCNA1
Gao et al. [36]
2008
16
Pyrosequencing
KLHL35, PAX5, PENK, SPDYA, LINE-1
Shitani et al. [40]
2012
DMH-chip
21
MSP
KLK10, OXGR1
Lu et al. [37]
2008
MeDIP-chip
6
MassArray
CYP24A1, DLX1, ZNF141, RASGRF2, ZNF382, TUBB6, NPR1,
RRAD, RUNX3, LOX, JAKMIP1, SFRP4, DUSP4, PARQ8, CYP7B1
Deng et al. [38]
2010
11
Pyrosequencing
AKR1B10, CENPH, MMP9, MMP12, PAGE4, S100A5, MMP2, NUPR1
Stefanska et al. [39]
2011
Validation method
Validated genes
Publication
Year
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Table 2 Genome wide methylation profiling in HCC
Beadarray
Discovery method
GoldenGate
Infinium 27K
Infinium 450K
HCC patients (n)
20
Methylight assay
APC
Archer et al. [49]
2010
38
Pyrosequencing
RASSF1, GSTP1, APC, GABRA5, LINE-1
Hernandez-Vargas et al. [43]
2010
45
Bisulfite sequencing
ERG, HOXA9
Hou et al. [47]
2013
3
COBRA and bisulfite sequencing
WNK2, EMILIN2, TLX3, TM6SF1, TRIM58, HIST1H4F, GRASP
Tao et al. [44]
2011
62
NIL
NIL
Yang et al. [118]
2011
13
NIL
NIL
Ammerpohl et al. [48]
2012
62
Pyrosequencing
CDKL2, STEAP4, HIST1H3G, CDKN2A, ZNF154
Shen et al. [42]
2012
63
Pyrosequencing
PER3
Neumann et al. [50]
2012
71
Pyrosequencing
NEFH, SMPD3
Revill et al. [53]
2013
66
NIL
NIL
Shen et al. [46]
2013
27
Pyrosequencing
GSTP1, RASSF1, BMP4, DLGAP1, GPR35
Song et al. [45]
2013
6
Bisulfite sequencing
DBX2, THY1
Zhang et.al. [41]
2013
COBRA, Combined bisulfite restriction analysis; MSP, Methylation specific PCR.
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Table 3 Commonly studied methylation markers in HCC
Gene
HCC patients (n)
Clinicopathological correlation
Validation method
Publication
Year
p16
28
Tumor stage
MSP
Yu et al. [56]
2003
18
Tumor stage
MSP
Shim et al. [59]
2003
20
Tumor differentiation
MSP
Qin et al. [66]
2004
50
Age, gender, virus infection (HBV/HCV)
MSP
Li et al. [62]
2004
44
HBV infection
MSP
Jicai et al. [63]
2006
60
Age, tumor stage, vascular invasion, virus infection (HBV/HCV)
MSP
Katoh et al. [61]
2006
58
Tumor stage
MSP
Su et al. [60]
2007
23
HBV infection (HBx)
MSP
Zhu et al. [65]
2007
265
Disease free survival
MSP
Ko et al. [70]
2008
118
Gender
Methylscreen
Wang et al. [119]
2012
50
Liver cirrhosis
MSP
Okochi et al. [77]
2003
51
HCV infection
MSP
Yang et al. [54]
2003
284
Age, tumor size, virus infection (HBV/HCV)
MSP
Ko et al. [73]
2008
77
HCV infection
COBRA
Nishida et al. [76]
2008
46
Liver cirrhosis, tumor size
MSP
Chu et al. [75]
2010
46
Tumor stage
MethyLight
Um et al. [72]
2011
29
Chemotherapy treatment
MSP
Saelee et al. [120]
2012
116
Age and gender
Methylscreen
Zhang et al. [74]
2013
60
Overall survival
MSP
Lee et al. [55]
2003
83
Alcohol consumption, gender
MSP
Zhang et al. [82]
2005
60
Gender, viral infection (HBV/HCV)
MSP
Katoh et al. [61]
2006
58
HBV infection, tumor stage
MSP
Su et al. [60]
2007
77
HCV infection
COBRA
Nishida et al. [76]
2008
166
HBV infection
Pyrosequencing
Lambert et al. [81]
2011
60
Overall survival
MSP
Lee et al. [55]
2003
32
Vascular invasion, recurrence
MSP
Ghee [91]
2005
SOCS1
GSTP1
CDH1
COBRA, Combined Bisulfite Restriction Analysis; HBV, Hepatitis B virus; HCV, Hepatitis C virus; HBx, Hepatitis B virus X protein; MSP, Methylation-specific PCR.
pathways that control cell proliferation and cell death
[79]. Promoter methylation of GSTP1 was first reported
in prostatic carcinoma back in 1994 [80]. Since then,
many groups reported such observation in other cancers,
including HCC. Analogous to earlier mentioned two
genes, GSTP1 was also found to be highly methylated in
HCC infected with either HBV or HCV compared to noninfected HCC [60,61,76,81]. Interestingly, methylation of
GSTP1 was significantly associated with gender [61,82]
and alcohol intake [82]. Also, study by Lee et al. managed
to show that patients with high GSTP1 methylation level
have worse overall survival outcome [55]. Although many
studies examined the association of GSTP1 methylation
with clinicopathological characteristics, only a few found
associations suggesting that GSTP1 methylation alone
may not be sufficient to serve as good single prognostic
predictor for HCC.
CDH1 is another well-known tumor suppressor gene
that was found methylated in many cancers [83-88]. It
was frequently methylated in HCC as well. Despite many
studies showed that methylation of CDH1 was higher in
HCC than adjacent non-tumors, it often was not significantly associated with clinical parameters [54,61,76,89,90].
Only Lee et al. reported that methylation of CDH1 was
linked with worse overall survival [55], and Ghee et al.
found that it was associated with vascular invasion and recurrence [91]. These reports suggest that probably CDH1
alone may not have the power to be an independent
prognostic factor. Notably, the concurrent methylation of
CDH1, GSTP1 and a few other genes were found to be
significantly associated with levels of AFP, recurrence free
survival (RFS) and tumor numbers (Table 4). This concordant methylation of a group of genes associated with
specific tumor characteristics is known as CIMP or CpG
island methylator phenotype [92]. Presently, many studies
have attempted to elucidate CIMP in various cancers,
including G-CIMP for gliomas [93], B-CIMP for breast
cancer [94], and C-CIMP for colorectal cancer [95]. In
HCC, concurrent methylation of various genes has been
associated with various clinical phenotypes (Table 4) and
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Table 4 CIMP studies in HCC
Genes used to define CIMP
HCC patients (n)
Clinicopathological
correlation
Validation
method
Publication
Year
ER, c-MYC, p14, p15, p16, p53, RB1,
RASSF1A, WT1
50
AFP level
MSP
Zhang et al. [96]
2007
E2F1, p15, p16, p21, p27, p300, p53,
RB, WT1
120
Metastasis
MSP
Zhang et al. [97]
2008
CDH1, p14, p15, p16, p21, RB1, RASSF1A,
SYK, TIMP3, WT1
60
Metastasis, tumor stage
MSP
Cheng et al. [98]
2010
CDH1, DAPK, GSTP1, p16, SOCS1, SYK, XAF1
65
AFP, RFS, tumor numbers
MSP
Wu et al. [100]
2010
GSTP1, MGMT, OPCML, p14, p15, p16, p73,
RARβ, SOCS1
115
OS, RFS
MSP
Li et al. [99]
2010
APC, CDH1, DKK, DLC1, RUNX3, SFRP1, WIF1
108
AFP level, DFS, Gender, HBV status,
tumor stage
MSP
Liu et al. [101]
2011
APC, GSTP1, HIC1, p16, PRDM2, RASSF1A,
RUNX3, SOCS1
177
DFS
Methylight
Nishida et al. [102]
2012
AFP, Alpha fetoprotein; DFS, Disease free survival; HBV, Hepatitis B virus; MSP, Methylation-specific PCR; OS, Overall survival; RFS, Recurrence free survival.
these could perhaps be known as CpG island methylator
phenotype for hepatocellular carcinoma, or Hep-CIMP.
Hep-CIMP associated with clinicopathological parameters
Presently, almost all studies that attempt to characterize
Hep-CIMP came from Chinese population and methylation level of these CIMP genes were determined by MSP
method. L-X, Wei led a team that published three studies
on Hep-CIMP from 2007 to 2010. They defined CIMP +
as samples with five or more methylated marker genes.
Interestingly, marker genes that they used to define CIMP
status varied across three studies (Table 4). Nonetheless,
they managed to associate CIMP status with elevated AFP
level (AFP ≥ 30 μg/L) [96], tumor metastasis [97,98], telomerase activity [97], tumor–node–metastasis (TNM) staging and overall survival [98].
Li et al. on the other hand, examined the methylation
status of nine marker genes, namely, p14, p15, p16, p73,
GSTP1, MGMT, RARβ, SOCS-1, and OPCML in 115
tumors. They defined CIMP + as HCC samples with six or
more such methylated genes. They showed that CIMP +
patients with TNM stage I have significantly poorer recurrence-free survival (RFS) and overall survival (OS)
compared to CIMP-, TNM stage I patients [99]. Wu et al.
also reported similar observation, despite their definition
of CIMP + differed slightly [100]. They considered a sample to be CIMP + as long as it has three or more methylated marker genes. Their study revealed that CIMP +
patients have shorter RFS compared to CIMP- patients.
On top of that, they also showed that tumor number
and pre-operative AFP levels were significantly higher in
CIMP + samples [100].
More recently, Liu et al. reported the CIMP status of
108 HCC tissues and plasma respectively, based on
methylation level of seven marker genes (Table 4). They
found good concordance of methylation status between
plasma and tissue samples. CIMP status in tumor tissues
and plasma were both significantly associated with clinicopathological parameters such as AFP level, TNM staging, gender and HBV infection [101]. Lastly, Nishida
et al. profiled methylation status of eight genes, namely,
HIC1, SOCS1, GSTP1, p16, APC, RASSF1, PRDM2 and
RUNX3, and found that these markers, collectively, were
associated with shorter time-to-occurrence of HCC
tumor [102]. Even though Nishida et al. did not report
CIMP status in their study, their analysis was similar to
the rest of the Hep-CIMP studies. Also worthy to note is
that these markers were carefully selected to represent
very early stage of HCC. This again emphasizes the potential clinical use of early CIMP + signature for diagnosis or prognosis purposes.
Currently, there is still no consensus on how we define
CIMP for HCC. As mentioned previously, all studies determined CIMP status based on their own set of genes,
even though we saw a few recurrent genes such as GSTP1
and p16. This is partly due to the technology limitation in
the past as most studies used MSP for methylation profiling. With genome-wide profiling methods become more
available, it is a matter of time that we can soon ascertain
the methylation markers that make up Hep-CIMP.
DNA methylation as potential blood biomarker
HCC is associated with high mortality rate mainly due to
late diagnosis [1]. Therefore, there is an urgent need to
identify promising tool that could diagnose the disease
early or be served as surveillance for patients at risk. DNA
methylation profile derived from blood samples could
potentially be such biomarker. The attempt to identify
methylation marker in blood dated back as early as 2003,
where Wong et al. used quantitative MSP to measure the
With clinicopathological correlation
Marker genes
HCC patients (n)
Clinicopathological correlation
Validation method
Samples used for DNA extraction
Publication
Year
DAPK, p16
64
AFP level
MSP
Serum
Lin et al. [104]
2005
RASSF1A
40
Tumor size
MSP
Plasma
Yeo et al. [106]
2005
LINE-1
85
OS, tumor size
COBRA
Serum
Tangkijvanich et al. [109]
2007
RASSF1A
85
DFS
Methylscreen
Serum
Chan et al. [107]
2008
CCND2
70
DFS
qMSP
Serum
Tsutsui et al. [105]
2010
APC, DKK, DLC1, CDH1, RUNX3,
SFRP1, WIF1
108
CIMP + associated with gender, HBV infection,
AFP level, tumor stage, DFS
MSP
Plasma
Liu et al. [101]
2011
APC, GSTP1, RASSF1A, SFRP1
72
OS (APC, RASSF1A)
Methylscreen
Plasma
Huang et al. [108]
2011
LINE-1
305
Increased risk of HCC
Pyrosequencing
White blood cells
Wu et al. [110]
2012
IGFBP7
136
Vascular invasion
MSP
Serum
Li et al. [115]
2013
XPO4
44
AFP level
MSP
PBMC
Zhang et al. [114]
2013
TFPI2
43
Tumor stage
MSP
Serum
Sun et al. [113]
2013
APC
23
Portal vein thrombosis
qMSP
Serum
Nishida et al. [112]
2013
Mah and Lee Biomarker Research 2014, 2:5
http://www.biomarkerres.org/content/2/1/5
Table 5 Methylation studies on DNA extracted from HCC blood samples
Without clinicopathological correlation
Marker genes
Clinicopathological correlation
Validation method
Samples used for DNA extraction
Publication
Year
p16
HCC patients (n)
29
NIL
qMSP
Serum and buffy coat
Wong et al. [103]
2003
p16
46
NIL
MSP
Serum
Chu et al. [121]
2004
2006
GSTP1
32
NIL
MSP
Serum
Wang et al. [122]
CDH1, p16, RASSF1A, RUNX3
8
NIL
MSP
Serum
Tan et al. [123]
2007
p16, p15, RASSF1A
50
NIL
MSP
Serum
Zhang et al. [124]
2007
GSTP1, RASSF1A
26
NIL
MSP
Serum
Chang et al. [125]
2008
RASSF1A
35
NIL
MSP
Serum
Hu et al. [126]
2010
APC, CDH1, FHIT, p15, p16
28
NIL
MSP
Plasma
Iyer et al. [127]
2010
DBX2, THY1
31
NIL
Bisulfite seq
PBMC
Zhang et al. [41]
2013
AFP, Alpha fetoprotein; COBRA, Combined Bisulfite Restriction Analysis; DFS, Disease free survival; HBV, Hepatitis B virus; MSP, Methylation-specific PCR; qMSP, Quantitative MSP; OS, Overall survival; PBMC, Peripheral
blood mononuclear cells; Seq, Sequencing.
Page 8 of 13
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methylation status of p16 in 29 HCC patients [103]. However, they did not perform any clinical association with
their data. In fact, many studies successfully measure the
aberrant methylation level of a marker gene in blood but
did not associate it with clinicopathological parameters.
These studies can be found in Table 5.
In this section, we will only highlight reports with significant clinical association. As shown in Table 5, Lin
et al. showed that among 64 patients, about 77% of them
have p16 methylation and 41% of them have DAPK
methylation. Both markers were associated with AFP
levels but no other parameters [104]. Tsutsui et al. found
that the serum of 39 out of 70 patients were positive for
methylated CCND2 gene. Patients of this group had
shorter disease free survival [105].
Yeo et al. used MSP and found that 17 out of 40 patients’ plasma (42.5%) had RASSF1A hypermethylation
and their methylation status was associated with tumor
size [106]. Chan et al. used another method, methylationsensitive restriction enzyme-mediated real-time PCR system, to detect RASSF1A methylation status in 85 HCC
sera. They found that 93% of them have hypermethylation
and their methylated status was associated with shorter
disease free survival and time-to-occurrence for HCC
[107]. Using similar detection method, Huang et al. also
showed that RASSF1A gene in 72 patients’ blood was
hypermethylated compared to normal controls and that
its methylation level was associated with poorer overall
survival [108].
Tangkijvanich et al. used Combined Bisulfite Restriction
Analysis (COBRA) method to measure the hypomethylation of LINE-1 in 85 patients’ sera. They reported that hypomethylation of LINE-1 was associated with HBV
infection, larger tumor size and more advance disease
stage [109]. Their study was further validated by Wu et al.,
where they used pyrosequencing to determine the methylation level of LINE-1 in 305 patients’ white blood cell
DNA [110]. They used logistic regression model to show
that hypomethylation of LINE-1 increased overall risk of
developing HCC. Recently, Gao et al. also reported that
LINE-1 was hypomethylated in 71 HCC tissues and was
associated with poorer prognosis [111]. These are few
studies that showed hypomethylation instead of hypermethylation as potential prognostic biomarker.
Following the availability of genome-wide methylation
profile, we also saw a sudden surge of methylation studies
based on patients’ sera. Within year 2013, four studies reported prognostic value of four different hypermethylated
genes. Briefly, Nishida et al. performed quantitative MSP
and showed that APC was more methylated in 23 HCC
sera compared to healthy volunteers. They also showed
that patients with higher APC methylation were associated with portal vein thrombosis [112]. Sun et al. detected
TFPI2 to be more methylated in 43 HCC sera and its level
Page 9 of 13
was associated with TNM stage [113]. Zhang et al. on the
other hand found XPO4 to be frequently methylated in 44
patients’ peripheral blood mononuclear cells. Their data
indicated that higher XPO4 methylation was associated
with higher AFP level [114]. Lastly, Li et al. discovered
that in HBV-associated HCC, IGFBP7 was more methylated compared to chronic hepatitis B patients and normal
controls. Also, its methylation status was associated with
vascular invasion in HCC [115].
Clearly, methylation of marker gene in HCC blood
DNA has potential prognostic value as shown by its association with clinicopathological data. However, most
current studies drawn conclusion from a retrospective
cohort. In order to translate these markers into actual
clinical use, proper prospective studies and validation
method are required.
Conclusions
Many genome wide methylation studies have confirmed
that HCC has distinct methylation profile (Table 2), and
some even showed that it is associated with different etiological factors such as HBV infection and alcohol consumption. Undeniably, the availability of these genome
wide data has allowed the discovery of many novel genes
with aberrant methylation, especially in recent years. As
shown in Additional file 1: Table S1, apart from the commonly studied genes mentioned in this review, there is
plethora of genes that were differentially methylated and
associated with clinicopathological data. Future studies
need to focus on collating current available data, shortlisting potential methylation markers by conducting proper
validation method [116,117] and defining well-characterized CIMP status of HCC. It is hoped that emerging
methylation markers can be used as diagnostic or prognostic marker for HCC in near future.
Additional file
Additional file 1: Table S1. List of methylation studies on HCC from
year 2003–2013.
Abbreviations
AFP: Alpha fetoprotein; CIMP: CpG island methylator phenotype;
COBRA: Combined bisulfite restriction analysis; DFS: Disease free survival;
DMH-chip: Differential methylation hybridization on microarray; DNMT: DNA
methyltransferase; HBV: Hepatitis B virus; HBx: Hepatitis B virus X protein;
HCC: Hepatocellular Carcinoma; HCV: Hepatitis C virus; MCAM: Methylated
CpG island amplification microarray; MeDIP-Chip: Methylated DNA
immunoprecipitation microarray; MSP: Methylation-specific PCR; OS: Overall
survival; PBMC: Peripheral blood mononuclear cells; PCR: Polymerase chain
reaction; QMSP: Quantitative MSP; RFS: Recurrence free survival.
Competing interests
All authors declare that they have no conflicts of interests.
Authors’ contributions
CGL and W-C researched data and wrote the manuscript. Both authors read
and approved the final manuscript.
Mah and Lee Biomarker Research 2014, 2:5
http://www.biomarkerres.org/content/2/1/5
Acknowledgements
This work is supported by grants from the National Medical Research Council
(NMRC) (NMRC/1131/2007 and NMRC/1238/2009), the BioMedical Research
Council (BMRC) (BMRC06/1/21/19/449) of Singapore and the Singapore
Millennium Foundation (SMF) as well as block fundings from the National
Cancer Centre, SINGAPORE and the DUKE-NUS Graduate Medical School to
A/Prof Caroline Lee.
Author details
1
Department of Biochemistry, Yong Loo Lin School of Medicine, National
University of Singapore, Singapore 117597, Singapore. 2Division of Medical
Sciences, Humphrey Oei Institute of Cancer Research, National Cancer Centre
Singapore, Level 6, Lab 5, 11 Hospital Drive, Singapore 169610, Singapore.
3
NUS Graduate School for Integrative Sciences and Engineering, National
University of Singapore, Singapore 117456, Singapore. 4Duke-NUS Graduate
Medical School, Singapore 169857, Singapore.
Received: 2 February 2014 Accepted: 7 March 2014
Published: 17 March 2014
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doi:10.1186/2050-7771-2-5
Cite this article as: Mah and Lee: DNA methylation: potential biomarker
in Hepatocellular Carcinoma. Biomarker Research 2014 2:5.
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